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5-2013
Learner Satisfaction in Online Learning: An
Analysis of the Perceived Impact of Learner-Social
Media and Learner-Instructor Interaction
Jeffery C. Andersen East Tennessee State University
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Recommended Citation
Andersen, Jeffery C., "Learner Satisfaction in Online Learning: An Analysis of the Perceived Impact of Learner-Social Media and Learner-Instructor Interaction" (2013). Electronic Theses and Dissertations. Paper 1115. https://dc.etsu.edu/etd/1115
Learner Satisfaction in Online Learning: An Analysis of the Perceived Impact of Learner-Social Media and Learner-Instructor Interaction
_____________________ A dissertation
presented to
the faculty of the Department of Educational Leadership and Policy Analysis East Tennessee State University
Johnson City, Tennessee
In partial fulfillment of the requirements for the degree Doctor of Education in Educational Leadership
________________________
by
Jeffery C. Andersen May 2013
_______________________
Dr. James Lampley, Chair Dr. Lee Daniels Dr. Jasmine Renner
Dr. Cindy Smith
ABSTRACT
Learner Satisfaction in Online Learning: An Analysis of the Perceived Impact of Learner-Social Media and Learner-Instructor Interaction
by
Jeffery C. Andersen
The purpose of this study was to determine the relationships between general course satisfaction, learner-instructor interaction, and the learner-social media interaction scores of participants. This study used an online survey with 60 questions to gather the participants’ demographic data, learner-instructor interaction data, learner-social media interaction data, and general course satisfaction data. Data from the survey were examined through the use of independent sample t-tests, one-way ANOVAs, and Pearson Correlations based on 10 participant demographic variables.
Of the 10 demographic variables, age, GPA, athletic team participation, and work status were found to have a statically significant relationship with the three constructs. The findings
indentified statistical significance between age, work status of participants, and the construct of learner-instructor interaction; between gender, athletic team participation, and the construct of social-media interaction; and between the age, GPA, work status, and the construct of general course satisfaction. Furthermore, learner-instructor interaction and learner-social media interaction had a statistically significant relationship with general course satisfaction. Overall, there was a strong positive correlation between both constructs of learner-instructor interaction
DEDICATION
This dissertation is dedicated to those individuals who gave me the support, time, and encouragement I needed throughout this journey. Thank you to my loving and understanding wife who gave me the encouragement, space, time, and understanding that I needed throughout this journey. Thank you to my son who allowed me to work, was understanding when Dad was not there, and gave me encouragement to continue my efforts in accomplishing this goal. I give thanks to my late parents who instilled in me a strong work ethic and understanding of the value of finishing what you start. To all of these individuals I am forever grateful.
ACKNOWLEDGEMENTS
I want to acknowledge the members of my dissertation committee, Drs. Terrence Tollefson (retired Chair), James Lampley (Chair), Lee Daniels, Jasmine Renner, and Cindy Smith, and thank them for their support and guidance throughout my program of study. A special thanks to Dr. Terrence Tollefson for his patience, understanding, and guidance during this
dissertation process. To Dr. James Lampley for his input and direction with the statistical processes; thank you for being a positive facilitator of learning. To Drs. Jasmine Renner, Lee Daniels, and Cindy Smith; thank you for your reviews and input into this dissertation.
I want to acknowledge Dr. Terrence Tollefson, the late Dr. Russell West, and Dr. Roland Deapner; these individuals were instrumental in the creation of the East Tennessee State
University cohort at Unicoi High School. Without this cohort I would not have been able to accomplish this goal and for that I am eternally grateful. To the members of this cohort; I thank you for your support and encouragement throughout our time together.
I want to acknowledge my colleagues who gave me moral support, ideas, and
encouragement at times when it was needed. I especially would like to acknowledge Dr. Beth Vogler, Dr. Craig Goforth, Dr. Don Russell, Mrs. Joy Clifton, and Mr. Bill Hamilton. Each gave of themselves in support of my educational goal, which makes working with them so enjoyable.
To my dissertation support team, Drs. Susan Twaddle and Sheila Smith; thank you for your time and energy in the statistical processes for this research and in the editing of my writing. You patience and willingness to share your skills and knowledge made the writing process more enjoyable. Finally, I would like to acknowledge Betty Ann Proffitt who was always willing to assist me and offer guidance throughout my time as a student.
TABLE OF CONTENTS Page ABSTRACT ... 2 DEDICATION ... 4 ACKNOWLEDGEMENTS... 5 LIST OF TABLES ... 8 LIST OF FIGURES ... 9 Chapter 1. INTRODUCTION ... 11
Statement of the Problem ... 16
Significance of Study ... 17
Research Questions ... 17
Assumptions, Limitations, and Delimitations ... 20
Definition of Terms ... 21
Overview of the Study ... 22
2. LITERATURE REVIEW ... 23
Introduction ... 23
Online Learning Environment... 23
Online Learning and Learner-Instructor Interactions ... 26
Online Learning and Learner-Social Media ... 30
Summary ... 32
3. RESEARCH METHOD ... 34
Data Collection ... 42
Data Analysis ... 42
Summary ... 43
4. RESULTS ... 44
Research Questions ... 44
5. SUMMARY OF FINDINGS, CONCLUSIONS, AND RECOMMENDATIONS ... 83
Summary of Findings ... 83
Key Findings ... 84
Learner-Instructor Interaction ... 84
Learner-Social Media Interaction ... 85
General Course Satisfaction ... 85
Conclusion ... 87
Recommendations for Practice... 88
Recommendations for Future Research ... 89
REFERENCES ... 90
APPENDICES ... 98
APPENDIX A: Survey Instrument ... 98
APPENDIX B: Permission Email from Elaine Strachota, Ed.D... 104
APPENDIX C: Introduction and Informed Consent ... 105
APPENDIX D: Survey Instrument Modifications ... 106
LIST OF TABLES
Table Page
1. Means and Standard Deviations of Student Classification Learner-Instructor
Interaction ... 66 2. Means and Standard Deviations of Student Classification Learner-Social Media
Interaction ... 68 3. Means and Standard Deviations of Student Classification General Course Satisfaction.. 70 4. Means and Standard Deviations with 95% Confidence Intervals of Pairwise
Differences for Learner-Instructor Interaction by Student Work Status ... 72 5. Means and Standard Deviations for Learner-Social Media Interaction by Student Work
Status... 74 6. Means and Standard Deviations with 95% Confidence Intervals of Pairwise
LIST OF FIGURES
Figure Page
1. Boxplot for Learner-Instructor Interaction by Gender ... 45
2. Boxplot for Learner-Social Media Interaction by Gender ... 47
3. Boxplot for General Course Satisfaction by Gender ... 48
4. Scatterplot for Learner-Instructor Interaction by Student Reported Age ... 49
5. Scatterplot for Learner-Social Media Interaction by Student Reported Age ... 50
6. Scatterplot for General Course Satisfaction by Student Reported Age ... 51
7. Scatterplot for Learner-Instructor Interaction by Student Reported GPA ... 52
8. Scatterplot for Learner-Social Media Interaction by Student Reported GPA ... 53
9. Scatterplot for General Course Satisfaction by Student Reported GPA ... 54
10. Boxplot for Learner-Instructor Interaction by Ethnicity ... 55
11. Boxplot for Learner-Social Media Interaction by Ethnicity ... 56
12. Boxplot for General Course Satisfaction by Ethnicity ... 57
13. Boxplot for Learner-Instructor Interaction by Athletic Team Participation ... 59
14. Boxplot for Learner-Social Media Interaction by Athletic Team Participation ... 60
15. Boxplot for General Course Satisfaction by Athletic Team Participation... 61
16. Boxplot of Learner-Instructor Interaction by Student Enrollment Status ... 63
17. Boxplot for Learner-Social Media Interaction by Student Enrollment Status ... 64
18. Boxplot for General Course Satisfaction by Student Enrollment Status ... 65
19. Boxplot for Learner-Instructor Interaction by Student Classification ... 67
20. Boxplot for Learner-Social Media Interaction by Student Classification ... 69
21. Boxplot for General Course Satisfaction by Student Classification ... 71
22. Boxplot for Learner-Instructor Interaction by Student Work Status ... 73
23. Boxplot for Learner-Social Media Interaction by Student Work Status ... 75
25. Scatterplot for Learner-Instructor Interaction by Number of Moodle Courses
Completed ... 78 26. Scatterplot for Learner-Social Media Interaction by Number of Moodle Classes
Completed ... 79 27. Scatterplot for General Course Satisfaction by Number of Moodle Classes
Completed ... 80 28. Scatterplot for General Course Satisfaction by Learner-Instruction Interaction ... 81 29. Scatterplot for General Course Satisfaction by Learner-Social Media Interaction ... 82
CHAPTER 1 INTRODUCTION
Because of the current global economic conditions, American higher education institutions are being challenged in unprecedented ways (US Department of Eduction, 2010). These institutions are seen as America’s way to compete by providing a pathway to good jobs and higher earning power for Americans (Allen & Seaman, 2010). Results from Hanna’s (2003) research showed that the demand for higher education was not being met. To meet that demand an unattainable building of institutions would need to occur. Valentine (1994) cited a study of higher education administrators completed by Basom and Sherritt, which revealed that meeting increased demands with decreasing resources, was the most pressing issue. Johnson, Levine, Smith, and Stone (2010) identified a further critical challenge to institutions of higher education to be that of providing high quality courses to a growing number of online learners with
decreasing resources. Increased access to higher education through governments is motivating students to seek out and enroll in online educational opportunities (Stewart, Bachman, & Johnson, 2010). These challenges have resulted in changes by institutions regarding how and when to deliver their product to the students who arrive at their doorsteps. According to LaBay and Comm (2004) for higher educational institutions to remain competitive, they must be offering online learning programs and courses.
In response to this increasing demand, more institutions of higher education are offering online learning. According to Allen and Seaman (2010) online enrollment has been growing faster than traditional face-to-face classroom instruction in recent years. In the fall of 2009, 5,600,000 students were enrolled in at least one online course, which represented a 21% increase over the highest online enrollment in any previous year. Their survey determined that one in four
students was taking online courses, 54% of institutions of higher education experienced an increase demand for online courses, 66% of institutions of higher education had an increased demand for new online courses and programs, and 73% of higher education institutions had an increased demand for existing online courses. These figures from the survey revealed that there was greater competition among institutions for the online learner and growth in the for-profit higher education sector. These challenges, increased demand for online learning, competition for online learners, and growth in the for-profit higher education sector will require that institutions of higher education consider what they deliver from a new perspective.
Low and USA Group, Inc. (2000) concluded that successful institutions shared the following three attributes: “…they focus on the needs of their students; they continually improve the quality of the educational experience, and they use student satisfaction data to shape their future directions” (p. 33). Methods for preparing students are changing and to accomplish this institutions are increasing the use of technology. Many students are facing increased work hours as they attend courses, increased family responsibilities, and a need for learning anytime and anywhere. Students’ needs are motivating institutions of higher learning to make significant changes in the way they deliver their learning programs (Frey, Webreck, & Steffens, 2004). This challenge to institutions of higher education requires a paradigm shift by the institution on how and when they deliver their courses and services. Hanna (2003) suggested that institutions of higher education would need to transform the structure of their processes and programs to be both flexible and more responsive to students’ needs. Administrators in institutions of higher education are recognizing that online learning programs are critical to their long-term strategy and are increasingly placing this in their institutional strategic planning (Kim & Bonk, 2006).
In 1993 researchers at The Sloan Consortium (2012) coined the now familiar term “asynchronous learning networks” to convey the idea that people learn at various times and places in everyday life (Moore, 2005). These researchers identified a quality framework and five pillars that support quality learning environments (Moore, 2005). Pillars of Quality are used as benchmarks for continuous improvement of teaching and learning in institutions of higher education. Two of the pillars are cost effectiveness and institutional commitment and student
satisfaction. These pillars are reflective of challenges being placed on institutions of higher
education. Student satisfaction reflects the satisfaction levels of students with their learning environments and cost effectiveness and institutional commitment reflects how well institutions manage their resources. Moore (2005) stated that 95% of all for-credit degree oriented
instruction in the country followed the Quality Framework model in their online learning environments. The identifiable goal in student satisfaction is based on how pleased students are with their experiences with online learning. The Cost Effectiveness and Institutional Commitment pillar identifies goals for continuously improving services while reducing costs. Institutions of higher education that achieve the goals of the Pillars in turn meet the needs of students, improve the quality of their programs, and are able to measure the satisfaction levels of their students. Concurrently, participating institutions are transforming their processes and methods in the delivery of quality online learning.
Online learning is changing the way in which higher education is viewed by students and faculty and is causing a paradigm shift within each group. Craig, Goold, Coldwell, and Mustard (2008) contended that online teaching is changing the roles of students and teachers. Students are increasingly referred to as consumers while demonstrating consumer-like behavior in their choice of learning environments (Howell, Williams, & Lindsay, 2011). Faculty roles in online
learning have been changing to include those of mentors, facilitators, counselors, and coaches. According to Johnson et al. (2010) research has suggested that “the role of the academy – and the way we prepare students for their future lives – is changing” (Johnson et al., 2010, p. 5). This creates a challenge for institutions of higher education to adopt teaching and learning practices that meet the increasing needs of online learners and faculty.
Duderstadt (1999) reported that online learning environments were changing from traditional faculty-centered to learner-centered environments. These changes are affecting both the mission and methodologies in which institutions operate. Such changes have created new challenges faced by institutions delivering online learning. One of the major challenges facing online learning is student attrition rates. Hill (2009) found that online learning program attrition rates were 10% to 20% higher than those in traditional face-to face classes. Hill suggested that such high attration rates represented critical issues that insitutions of higher education must face. Shaik (2009) also reported that low retention rates represented significant losses of revenue for the insitutions and could have a negative impact on their financial health. Herbert (2006) stated “…a key issue for postsecondary institutions is that of trying to find ways in which student retention in online courses can be improved” (p. 2). Conducting research to determine more effective ways to decrease attrition rates of online learners is critical to both the financial resources and the perceived program quality for institutions of higher education.
Heyman (2010) contended that one of the largest challenges to providers of online learning was that of reducing attrition rates. Research on student retention has been conducted for many years and the focus until recently has been on the traditional student in higher
levels and the relationship to retention (Herbert, 2006). Researchers have studied at the satisfaction levels of students based on student characteristics, course design structure, course delivery methods, and student expectations. Faculty responsible for the development and design of online learning should take into account students’ satisfaction, which could result in increased quality of online learning programs (Sampson, Leonard, Ballenger, & Coleman, 2010). Stewart et al. (2010) suggested that most studies had focused on demographic variables of students and few had focused on the relationship and expectations of the online learners. In order to better serve online students, institutions must understand how satisfied online learners are with their educational experience (Noel-Levitz, Inc., 2009).
With the increase of offerings in online learning, there has been little research to investigate learners’ satisfaction in online learning environments (Craig et al., 2008; Hill & Raven, 2000; Mykota & Duncan, 2007; Singh, 2005). Research on satisfaction levels has identified factors having a direct impact on the satisfaction levels of online learners. Vesely, Bloom, and Sherlock (2007) found that increased interaction between faculty and students resulted in an increased satisfaction level for online learners. Song (2004) found a positive correlation between vocational effectiveness and the teaching and learning process and
significant differences in satisfaction scores based on the student characteristics such as marital status and reason for taking online courses. Craig et al. (2008) suggested that a mismatch between students’ expectations and their experiences decreased student satisfaction levels. Scalese (2001) and Carr (2000) reported that those institutions with higher student satisfaction levels were more likely to reduce their attrition levels. Noel-Levitz, Inc. (2009) found in their study of online learners that higher graduation rates were associated with more highly satisfied students. Hawkins (2009) found that when students’ satisfaction levels increased the retention
rate increased as well. When institutions have a full understanding of the factors that affect online learners satisfaction levels, retention rates should increase.
Statement of the Problem
Higher education institutions are being challenged by an increasing demand for programs and courses. To meet this challenge, institutions have turned to technology for assistance with the delivery of their programs. A paradigm shift in higher education has occurred in how learning is delivered to students (El Mansour & Mupinga, 2007). Despite the increased number of institutions providing online learning programs, one of the largest challenges to higher education is the retention of students in online programs (Heyman, 2010). Understanding the factors that increase student satisfaction scores in online learners could provide educators with data needed for the design of online courses and programs that will improve the retention rate of online learners. “By collecting satisfaction data from online learners on a regular basis, campuses are able to determine where they are best serving these students and where there are areas for improvement” (Noel-Levitz, Inc., 2009, p. 2). Because there are a limited number of studies addressing student satisfaction and the use of social media, this study will add knowledge about improving technology in online classes. Finally, the additional knowledge should provide
institutions with a foundation in which to improve and enhance their online program delivery and positively affect student satisfaction.
The purpose of this study was to determine if there is a relationship between the use of social-media and students’ satisfaction scores in online classes at the participating college. The second question to be addressed will be to identify the relationship between perceived instructor-learner interaction and students’ satisfaction scores at the participating college. The research
questions addressed in this study will assist in understanding relationship of these variables, use of social media and instructor-student interaction, to student satisfaction scores in online courses.
Significance of Study
Zawacki-Richter, Bäcker, and Vogt (2009) reported that interaction and communication in online learning communities were frequently rated as the most important factors. Easton (2003) indicated that the instructors’ new role required an ability to engage students through virtual communications. The findings of this study should provide online faculty members with course design information that can lead to increased student satisfaction. By surveying students’ satisfaction with online learning, best practices to the approach of delivering online learning courses may be identified.
Research Questions The following research questions guided this study.
Research Question 1: Is there a significant difference in the Learner-Instructor Interaction Scores between male and female students at the participating college?
Research Question 2: Is there a significant difference in the Learner-Social Media Interaction Scores between male and female students at the participating college?
Research Question 3: Is there a significant difference in the General Course Satisfaction Scores between male and female students at the participating college?
Research Question 4: Is there a significant relationship of the Learner-Instructor Interaction Scores among students’ reported ages at the participating college?
Research Question 5: Is there a significant relationship of the Learner-Social Media Interaction Scores among students’ reported ages at the participating college?
Research Question 6: Is there significant relationship of the General Course Satisfaction Scores among students’ reported ages at the participating college?
Research Question 7: Is there a significant relationship of the Learner-Instructor Interaction Scores among the grade-point-average (GPA) groups at the participating college? Research Question 8: Is there a significant relationship of the Learner-Social Media Interaction
Scores among the grade-point-average (GPA) groups at the participating college?
Research Question 9: Is there a significant relationship of the General Course Satisfaction Scores among the grade-point-average (GPA) groups at the participating college?
Research Question 10: Is there a significant difference in the Learner-Instructor Interaction Scores between White and minority students at the participating college?
Research Question 11: Is there significant difference in the Learner-Social Media Interaction Scores between White and minority students at the participating college?
Research Question 12: Is there a significant difference in the General Course Satisfaction Scores between White and minority students at the participating college?
Research Question 13: Is there a significant difference in the Learner-Instructor Interaction Scores between college athletic team members and non-college athletic team members at the participating college?
Research Question 14: Is there a significant difference in the Learner-Social Media Interaction Scores between college athletic team members and non-college athletic team members at the participating college?
Research Question 15: Is there a significant difference in the General Course Satisfaction Scores between college athletic team members and non-college athletic team members at the
Research Question 16: Is there a significant difference in the Learner-Instructor Interaction Scores between full-time students and part-time students at the participating college? Research Question 17: Is there a significant difference in the Learner-Social Media Interaction
Scores between full-time students and part-time students at the participating college? Research Question 18: Is there a significant difference in the General Course Satisfaction Scores
between full-time students and part-time students at the participating college? Research Question 19: Is there a significant difference in the Learner-Instructor Interaction
Scores among students classified as Freshman, Sophomore, Junior, Senior, or Graduate Student at the participating college?
Research Question 20: Is there a significant difference in the Learner-Social Media Interaction Scores among students classified as Freshman, Sophomore, Junior, Senior, or Graduate Student at the participating college?
Research Question 21: Is there a significant difference in the General Course Satisfaction Scores among students classified as Freshman, Sophomore, Junior, Senior, or Graduate Student at the participating college?
Research Question 22: Is there a significant difference in the Learner-Instructor Interaction Scores among students who work full time, work part time, or do not work at the participating college?
Research Question 23: Is there a significant difference in the Learner-Social Media Interaction Scores among students who work full time, work part time, or do not work at the participating college?
Research Question 24: Is there a significant difference in the General Course Satisfaction Scores among students who work full time, work part time, or do not work at the participating college?
Research Question 25: Is there a significant relationship of the Learner-Instructor Interaction Scores among the groups of completed Moodle delivered classes at the participating college?
Research Question 26: Is there a significant relationship of the Learner-Social Media Interaction Scores among the groups of completed Moodle delivered classes at the participating college?
Research Question 27: Is there a significant relationship of the General Course Satisfaction Scores among the groups of completed Moodle delivered classes at the participating college?
Research Question 28: Is there a significant relationship between Learner-Instructor Interaction Scores and General Course Satisfaction Scores?
Research Question 29: Is there a significant relationship between Learner-Social Media Interaction Scores and General Course Satisfaction Scores?
Assumptions, Limitations, and Delimitations
This study is limited by the appropriateness of the electronic survey in determining the participants understanding of instructor interaction, social media, and course satisfaction constructs. It is assumed that the survey used for data collection is valid and reliable. It is also assumed that the methodology adequately addressed the research questions. In addition it is assumed that the statistical tests were appropriate and possessed the necessary power to detect
the survey honestly and that the sample was representative of the population. This study is also limited by the usefulness of the results to the stakeholders.
This study is delimited to the students enrolled in a Moodle delivered course during July and October, 2012. This study is further delimited by the theoretical framework that was selected for the research. Learner-instructor interaction, learner-social media interaction, and general course satisfaction were measured on a Likert-type scale with an instrument especially designed for this study. This study is also delimited to participants who choose to return a completed survey. The results may not be generalized other online learning communities.
Definition of Terms
Asynchronous Learning Networks: technology-enabled networks for communications and learning communities (Moore, 2005, p. 9).
Blended or Hybrid Course: having between 30% and 80% of the course content delivered online (Allen & Seaman, 2010, p. 5). For the purposes of this study a blended class uses both face-to-face and online discussions.
Face-to-Face Class: courses in which zero to 29% of the content is delivered online; this
category includes both traditional and web facilitated courses (Allen & Seaman, 2010, p. 5).
Moodle: an open source software package used for producing internet-based courses and web sites (Moodle, n.d.).
Online Course: defined as those in which at least 80% of the course content is delivered online (MHC). An online course usually has no face to-face-meetings. For the purposes of this study this is a fully online class.
Web Facilitated Course: Course that uses web-based technology to facilitate what is essentially a face-to-face course. May use a course management system (CMS) or web pages to post the syllabus and assignments (Allen & Seaman, 2010, p. 5). Web facilitated courses deliver 1% to 29% of the content online.
Overview of the Study
This study is organized into five chapters. Chapter 1 – Introduction – contains a description of The Population, the Statement of The Problem, Significance of the Study,
Research Questions, Limitations and Delimitations, Definition of Terms, and an Overview of the Study. Chapter 2 – Literature Review – contains review of the literature, Online Learning,
Online Learning and Learner-Instructor Interactions, Online Learning and Learner-Social Media, and a Summary. Chapter 3 – Research Method – describes how the research was done including the Research Questions and Null Hypotheses, Instrumentation, Population, Data Collection, Data Analysis, and a Summary. Chapter 4 – Analysis of Data – reports the findings of the study and an analysis of the data for each research question. Chapter 5 – Summary of Findings,
Conclusions, and Recommendations – provides a Summary of the Findings for each research question, Conclusions for each research question, Recommendations for Future Research, and Recommendations for Practice.
CHAPTER 2 LITERATURE REVIEW
Introduction
Institutions of higher education are facing many new challenges that include how colleges and universities deliver their educational services. These challenges stem in part from the development of technologies that are changing the way higher education institutions operate. Christensen and Eyring (2011) suggested that as a technology, online learning has been
changing, including the ways in which higher education delivers its courses, the demographics of the learners, and the organizational structures of higher education institutions. A report by
McCarthy, Samors, the Association of Public and Land-Grant Universities, and the Alfred P. Sloan Foundation (2009) focused on including online learning to achieve institutional goals and missions. To capitalize on those challenges, many higher education institutions have transformed the ways they create and deliver their educational services. They do this by establishing online learning courses and programs. Johnson et al. (2010) found that the role of colleges and
universities had increasingly focused on key goals and adapting teaching and learning practices to meet the needs of current learners. Lokken and Womer (2007) reported that 70% of the responding institutions stated that demands for online courses were exceeding their current offerings. Colleges and universities are embracing and managing new educational delivery challenges through the creation of online learning programs.
Online Learning Environment
Allen and Seaman (2010) reported that in the fall of 2009 students who were taking online courses had increased by 1,000,000 since 2008. That was the largest single year increase
continues to increase, so are the roles of the faculty and demographics of students. Kim and Bonk (2006) found that successful online instructors were aligned with constructivist principles in the design of online learning communities. They also concluded that the alignment involved the inclusion of interactive project-based learning. Easton (2003) found that online learning was changing the faculty role and limiting their face-to-face contact with students. The faculty role was changing to one of a learning facilitator, software technology expert, and increasingly a specialist in curriculum design. Johnson et al. (2010) suggested that online technology was challenging faculty members to revisit their roles as educators. Faculty members were finding it necessary to rethink the way they designed and integrated technology into their instruction (Howell et al., 2011). Garrison (2000) stated that the challenge to online faculty would be the understanding of delivering learning at a distance. Song and Hill (2009) contended that the instructor’s effective facilitation and guidance was needed to produce successful online learning environments. Finally, Glahn and Gen (2002) suggested that the delivery of online learning had moved from process innovation to the adoption of appropriate teaching and learning strategies to ensure student success.
Online learning has been changing the way higher education institutions are delivering their courses, including the removal of geographic barriers. Students with previously limited access to higher education now find an increasing number of educational opportunities. This has resulted in a change in the demographics of the students involved in higher education. A study by the Demos Foundation (as cited in American InterContinental University, 2010) revealed that the number of students under 24 working full-time had increased by 18% since 1972. A US Department of Education study (as cited in American InterContinental University, 2010) showed
increasingly have been searching for online options that are flexible and can fit into work and family commitments (American InterContinental Universtiy, 2010; Herbert, 2006; LaBay & Comm, 2004; Noel-Levitz, Inc., 2009). The changing demographics of the college student population will affect how faculty members plan and deliver online programs. In turn, this will require programs to be designed to incorporate new technologies designed to increase online learning satisfaction.
Changing the method of course delivery to online has required an explosion of new technology to be created in that endeavor. Social media comprises a set of technologies that is increasingly used by students and faculty within online learning environments. Social media has been described as “…the potential to transform from a way of pushing content outward to a way of inviting conversation, of exchanging information, and of invoking unparalleled individual, industry, societal, and even global change” (Moran, Seaman, & Tinti-Kane, 2011, p. 4).
According to Boyd and Ellison (2007) web-based services that allow individuals to (1) construct a public or semipublic profile within a bounded system, (2) articulate a list of other users with whom they share a connection, and (3) view and traverse their list of connections and those made by others within the system.
Smith, Caruso, and the Educause Center for Applied Research (2011) reported that the use of social media by college students continued to remain high. Over 90% of the student respondents from their study indicated that they used social media on a daily basis. Moran et al. (2011) reported that more that 80% of faculty members were incorporating some form of social media into their teaching. Faculty members have also stated that social media represented a valuable tool for collaborative learning.
Online Learning and Learner-Instructor Interactions
Because the online instructor does not have the advantage of meeting each learner face-to-face, the challenge is to create various positive interactive activities in the asynchronous course environment. Woollen and Rabe-Hemp (2009) completed a mixed methodology study to investigate if students’ expectations affect their satisfaction with an online course. Data collected indicated a higher level of dissatisfaction among students with a lack of contact with the faculty member. Watwood, Nugent, and Deihl (2009) proposed from their literature review of online learning that it was the method of delivery and the interactions that were different in the online learning environment. Vesely et al. (2007) explored the importance of the development of an online learning community. A survey was used to gather data from 62 university participants. The survey was delivered by email and administered near the end of the semester to allow participants some time for reflection. Data analysis revealed that 85% of the participants perceived that being part of a learning community assisted in the students’ success. The
participants identified key elements of the learning community as (a) purposeful communication involving encouragement and support, (b) comfortable exchange of ideas in an organized
fashion, and (c) a sense of shared purpose. The study emerged with two conclusions: (1) The development of learning communities was encouraged by including structure and collaborative activities, and (2) the inclusion of opportunities for intentional and supportive activities (Vesely et al., 2007). According to Vesely et al. it is incumbent upon the online faculty to play a large leadership role in building the learning community. Instructors may begin this process by modeling the appropriate behavior and creating the environment through course design. The faculty members’ presence (interactive activities) must be frequent and effective.
Mupinga, Nora, and Yaw (2006) studied the learning styles and expectations of the online learner and how they can be incorporated into the design of online instruction. They used the Myers-Briggs Cognitive Style Inventory and a survey with one open-ended question: “What are your needs and expectations as an Internet student?” (Mupinga et al., 2006, p. 180). The results revealed that the online learner expected instructor interaction. The top two responses can be grouped into communication with the instructor and instructor feedback. Eighty-three percent of the participants stated that they expected the instructor to communicate with them on a regular basis and to make prompt responses to inquiries. They further defined prompt as a maximum of a 24-hour confirmation of receipt of submitted assignments. Seventy-six percent expected the assignments to be graded immediately or at least within 48 hours. The study discussion identified that other instructor interaction expectations were (a) guidance with sample assignments posted, (b) advance course information, and (c) life challenges would be acknowledged by the instructor. All of the expectations represent actions the learner required of the instructor to increase their connection with the course (Mupinga et al., 2006).
A 2007 study by Dennen, Darabi, and Smith examined the perceived importance of 19 instructor actions in online courses, according to both instructors and students. The study participants included 32 online instructors and 171 students from a private university. Students and instructors were asked to rate each of 16 items indicating their perception of the items based on their role. The instructors rated the items on the basis of importance to student performance and satisfaction and the students rated the importance of the items on relationship to the effectiveness of instructor practices. The results mirrored the Mupinga et al. 2006 study by identifying extensive feedback (email, feedback, and forum posting) and information needs (examples and course materials) as having high importance by both the instructors and students.
The discussion highlights the implied belief that learners feel more satisfied when their interpersonal communication needs are met (Dennen et al., 2007).
Sher (2009) studied the relationship between student learning and interaction dynamics in an online learning environment. The sample (208 students at US East Coast University)
consisted of students enrolled in 30 class sections in Tourism Administration, project
Management, and Health Sciences during the spring 2003 semester. The research identified a significant statistical relationship between the student-instructor interaction and student
satisfaction in online learning environments. Sher (2009) concluded the research suggesting that online learning programs must “…provide students with what is valued in education: interaction with instructors and other students” (p. 117).
Ali, Ramay, and Shahzad (2011) compared the associations between several variables of online learning environment with student satisfaction. The variables used in this relationship study were (a) instructor’s performance, (b) course evaluation, and (c) student-instructor interaction. The sample of 245 students at Allama Iqbal Open University completed a survey administered at the university. The results revealed that student-instructor interaction was the strongest variable in predicting student satisfaction, followed by instructor’s performance, and finally course evaluations. Over 68% of the participants indicated that instructor encouragement for them to become actively involved in course discussion was an important factor. Once again, the researchers indicated the importance of the instructor’s actions in the online learning
environment and its relationship with student satisfaction.
Sun et al. (2008) researched the critical factors associated with learner satisfaction with online learning. Of the 645 surveys distributed to students in 16 courses, only 295 surveys were
returned and usable for the study. Seven of the 13 variables had a statistically significant relationship with online learner satisfaction:
1. online course quality, 2. diversity in assessment, 3. learner perceived ease of use,
4. learner perceived usefulness of online learning, 5. instructor attitude toward online learning, 6. online course flexibility, and
7. learner computer anxiety (Sun et al. 2008).
An interesting finding of this study, which is different from findings discussed in other studies, was that there was not a statistically significant relationship between the instructor’s response time and online learner satisfaction. The researchers suggested it might be that students were working and may not have noticed the instructor’s timeliness (Sun et al., 2008).
A study of 917 undergraduate students, using the Distance Education Learning
Environments Survey (DELES), was completed by Sahin (2007). Sahin explored the relationship between student satisfaction and six predictor variables where four were found to be statistically significant and positively related to student success:
personal relevance, instructor support, active learning, and authentic learning.
Student autonomy and student interaction and collaboration were not found to be statistically significant related to student success. Sahin (2007) suggested that timely help by the instructor,
useful instructor feedback, and easy communication by the instructor were key factors in student satisfaction.
Online Learning and Learner-Social Media
Lokken and Womer (2007) reported that online learning is increasingly attractive to
Milennials who are also known as Net Generation students. Such students are well versed and
active in the use of social media such as Facebook, Twitter, and iTunes (Gleason, 2008; Herbert, 2006; Sampson et al., 2010). Many online instructors have been incorporating the use of social media into their courses as a way to engage online learners. Tinti-Kane, Seaman, and Levy (2011) found that 30% of faculty members who completed their social media use survey reported the use of social media to communicate with students. While there have been few studies on the relationship between social media and student satisfaction, there have been some that have shown a positive relationship between the two. Rath (2011) completed a study to explore the use of Twitter in an online learning environment. The study involved 39 students taking an online class that incorporated Twitter into the learning environment. At the end of 13 weeks, the participants received a 10-question multiple-choice survey with one open-ended question. Data from that survey revealed that using Twitter in the course was associated with an 86% agreement that a sense of community was created. Other findings of the study, when compared to other social media, such as, Facebook and LinkedIn, revealed that 38% of the participants suggested there was no uniqueness to Twitter as a social medium (Rath, 2011). Lin and van’t Hooft (2008) researched the impact blogs have on student satisfaction and found that the increased level of interactivity of blogs increased the students’ learning satisfaction. Lin and van’t Hooft (2008) used mixed methods in their study of 28 undergraduate students who were enrolled in a Taiwan
blended learning environments. According to Lin and van’t Hooft educators should be encouraged to design learning environments that facilitate social interaction.
Another study by Rutherford (2010) that examined the use of social media in an online learning environment was completed. That study found a positive correlation between students’ use of a variety of social media resources and how students evaluated the quality of their learning experience and overall program quality. The participants were 675 teachers in an 8-month
preservice education program. The study assessed the perceived impact of social media use on student engagement. The survey used was similar to the National Survey of Student Engagement (NSSE) and was delivered to the participants through an email prompt. The social media
identified by participants as being used most often for course work collaboration included (a) email, (b) Twitter, (c) LMS, (d) Facebook, and (e) wikis. Rutherford concluded that
understanding the use of social media resources may assist in motivating lowly engaged students. A 2002 study by Thurmond, Wambach, Connors, and Frey compared the relationship between each of several environmental variables in an online learning environment to student satisfaction. The environmental variables were based on the principles discussed by Chickering and Gamson (1987) in Seven Principles for Good Practice in Undergraduate Education.
Included in these are engaging in active learning and providing quick feedback. The participants were students from three different university nursing programs who completed a 57-item
questionnaire to evaluate their online nursing courses. The findings revealed that there is a statistically significant relationship between the environmental variables (communication from and with the instructor) to student satisfaction. The study also found that a strong predictor of student satisfaction was having opportunities to work in teams or groups (Thurmond et al., 2002).
To examine social interaction, Oestmann and Oestmann (2005) assessed the association between class size and learner interaction in forums and discussions. The participants of this study were enrolled in five sections of the same course. The classes ranged in size from less than 10 to more than 20 students in a class. The study findings indicated that there were more
significantly different substantive discussion posts in large classes as compared to small classes. A discussion of the study revealed that this datum could be used by online administrators in the structuring of class size and by instructors in creating online learning environments.
Summary
A review of the literature has shown that the instructor’s role has changed with the onset of online learning. The use of social media and interactions that the instructor should have with students has been identified as highly important. The research shows that students want to interact with the instructor and peers and expect this in an online learning environment. Delaney, Johnson, Johnson, and Treslan (2010) investigated students’ perceptions of effective teaching and how instructors demonstrated these characteristics. The responses to an open-ended online survey from both face-to-face and online students were grouped into nine categories of effective instructional behaviors. Both research groups identified three effective instructional behaviors associated with the learner-instructor interaction: approachable, engaging, and communicative and responsive.
Dabbagh (2007) described in her article about emerging characteristics and pedagogical implications for the online learner that the instructor should focus on designing online learning environments that engage the learner. Further, Baghdadi (2011) suggested that a best practice for an online instructor included “The online instructor must actively participate in all dimensions of
variables of the online learning environment are associated with student satisfaction: 1) Use of media in the online learning environment and 2) Instructor-learner interaction in online learning environments.
CHAPTER 3 RESEARCH METHOD
The purpose of this study was to determine the relationships between or among the mean general course satisfaction, learner-instructor interaction, and the learner-social media interaction scores of participants. This chapter identifies the quantitative research design, population,
instrument used, data collection methods, and data analysis methods. This quantitative study consisted of a data analysis based on the responses to an online survey that I sent electronically to students enrolled in Moodle delivered courses during the months of July and October 2012. The instrument used is a modification of The Online Satisfaction Survey (see Appendix A) developed by Strachota (2003). I asked the participants surveyed to respond to specific questions about student-instructor interactions and instructor use of social media in their online courses.
Research Questions and Null Hypotheses
Twenty-nine research questions and associated null hypotheses were used to guide this study.
Research Question 1. Is there a significant difference in the Learner-Instructor Interaction Scores between male and female students at the participating college?
Ho1: There is no significant difference in the Learner-Instructor Interaction Scores between male and female students at the participating college.
Research Question 2. Is there a significant difference in the Learner-Social Media Interaction Scores between male and female students at the participating college?
Research Question 3. Is there a significant difference in the General Course Satisfaction Scores between male and female students at the participating college?
Ho3 There is no significant difference in the General Course Satisfaction Scores between male and female students at the participating college.
Research Question 4. Is there a significant relationship of the Learner-Instructor Interaction Scores among students’ reported ages at the participating college?
Ho4: There is no significant relationship of the Learner-Instructor Interaction Scores among students’ reported ages at the participating college.
Research Question 5. Is there a significant relationship of the Learner-Social Media Interaction Scores among students’ reported ages at the participating college?
Ho5: There is no significant relationship of the Learner-Social Media Interaction Scores among students’ reported ages at the participating college.
Research Question 6. Is there significant relationship of the General Course Satisfaction Scores among students’ reported ages at the participating college?
Ho6: There is no significant relationship of the General Course Satisfaction Scores among students’ reported ages at the participating college.
Research Question 7. Is there a significant relationship of the Learner-Instructor Interaction Scores among the grade-point-average (GPA) groups at the participating college?
Ho7: There is no significant relationship of the Learner-Instructor Interaction Scores among the grade-point-average (GPA) groups at the participating college.
Research Question 8. Is there a significant relationship of the Learner-Social Media Interaction Scores among the grade-point-average (GPA) groups at the participating college?
Ho8: There is no significant relationship of the Learner-Social Media Interaction Scores among the grade-point-average (GPA) groups at the participating college.
Research Question 9. Is there a significant relationship of the General Course Satisfaction Scores among the grade-point-average (GPA) groups at the participating college?
Ho9: There is no significant relationship of the General Course Satisfaction Scores among the grade-point-average (GPA) groups at the participating college. Research Question 10. Is there a significant difference in the Learner-Instructor Interaction Scores between White and minority students at the participating college?
Ho10: There is no significant difference in the Learner-Instructor Interaction Scores between White and minority students at the participating college.
Research Question 11. Is there significant difference in the Learner-Social Media Interaction Scores between White and minority students at the participating college?
Ho11: There is no significant difference in the Learner-Social Media Interaction Scores between White and minority students at the participating college.
Research Question 12. Is there a significant difference in the General Course Satisfaction Scores between White and minority students at the participating college?
Ho12: There is no significant difference in the General Course Satisfaction Scores between White and minority students at the participating college.
Research Question 13. Is there a significant difference in the Learner-Instructor Interaction Scores between college athletic team members and non-college athletic team members at the participating college?
Ho13: There is no significant difference in the Learner-Instructor Interaction Scores between college athletic team members and non-college athletic team members at the participating college.
Research Question 14. Is there a significant difference in the Learner-Social Media Interaction Scores between college athletic team members and non-college athletic team members at the participating college?
Ho14: There is no significant difference in the Learner-Social Media Interaction Scores between college athletic team members and non-college athletic team members at the participating college.
Research Question 15. Is there a significant difference in the General Course Satisfaction Scores between college athletic team members and non-college athletic team members at the
participating college?
Ho15: There is no significant difference in the General Course Satisfaction Scores between college athletic team members and non-college athletic team members at the participating college.
Research Question 16. Is there a significant difference in the Learner-Instructor Interaction Scores between full-time students and part-time students at the participating college?
Ho16: There is no significant difference in the Learner-Instructor Interaction Scores between full-time students and part-time students at the participating college.
Research Question 17. Is there a significant difference in the Learner-Social Media Interaction Scores between full-time students and part-time students at the participating college?
Ho17: There is no significant difference in the Learner-Social Media Interaction Scores between full-time students and part-time students at the participating college. Research Question 18. Is there a significant difference in the General Course Satisfaction Scores between full-time students and part-time students at the participating college?
Ho18: There is no significant difference in the General Course Satisfaction Scores between full-time students and part-time students at the participating college. Research Question 19. Is there a significant difference in the Learner-Instructor Interaction Scores among students classified as Freshman, Sophomore, Junior, Senior, or Graduate Student at the participating college?
Ho19: There is no significant difference in the Learner-Instructor Interaction Scores among students classified as Freshman, Sophomore, Junior, Senior, or Graduate Student at the participating college.
Research Question 20. Is there a significant difference in the Learner-Social Media Interaction Scores among students classified as Freshman, Sophomore, Junior, Senior, or Graduate Student at the participating college?
Ho20: There is no significant difference in the Learner-Social Media Interaction Scores among students classified as Freshman, Sophomore, Junior, Senior, or Graduate Student at the participating college.
Research Question 21. Is there a significant difference in the General Course Satisfaction Scores among students classified as Freshman, Sophomore, Junior, Senior, or Graduate Student at the participating college?
Ho21: There is no significant difference in the General Course Satisfaction Scores among students classified as Freshman, Sophomore, Junior, Senior, or Graduate Student at the participating college.
Research Question 22. Is there a significant difference in the Learner-Instructor Interaction Scores among students who work full time, work part time, or do not work at the participating college?
Ho22: There is no significant difference in the Learner-Instructor Interaction Scores among students who work full time, work part time, or do not work at the participating college.
Research Question 23. Is there a significant difference in the Learner-Social Media Interaction Scores among students who work full time, work part time, or do not work at the participating college?
Ho23: There is no significant difference in the Learner-Social Media Interaction Scores among students who work full time, work part time, or do not work at the
participating college.
Research Question 24. Is there a significant difference in the General Course Satisfaction Scores among students who work full time, work part time, or do not work at the participating college?
Ho24: There is no significant difference in the General Course Satisfaction Scores among students who work full time, work part time, or do not work at the participating college.
Research Question 25. Is there a significant relationship of the Learner-Instructor Interaction Scores among the groups of completed Moodle delivered classes at the participating college?
Ho25: There is no significant relationship of the Learner-Instructor Interaction Scores among the groups of completed Moodle delivered classes at the participating college.
Research Question 26. Is there a significant relationship of the Learner-Social Media Interaction Scores among the groups of completed Moodle delivered classes at the participating college?
Ho26: There is no significant relationship of the Learner-Social Media Interaction Scores among the groups of completed Moodle delivered classes at the participating college.
Research Question 27. Is there a significant relationship of the General Course Satisfaction Scores among the groups of completed Moodle delivered classes at the participating college?
Ho27: There is no significant relationship in the General Course Satisfaction Scores among the groups of completed Moodle delivered classes at the participating college.
Research Question 28. Is there a significant relationship between Learner-Instructor Interaction Scores and General Course Satisfaction Scores?
Ho28: There is no significant relationship between Learner-Instructor Interaction Scores and General Course Satisfaction Scores.
Research Question 29. Is there a significant relationship between Learner-Social Media Interaction Scores and General Course Satisfaction Scores?
Instrumentation
The survey instrument used to gather the data on student satisfaction in online courses was a modified version of The Online Student Satisfaction Survey (see Appendix A) created by Strachota (2003). Strachota tested this survey instrument for reliability and validity through the use of field experts and a pilot test. I completed a survey development activity with 15
undergraduate students at the participating college during the Spring 2012 semester. This activity was completed to ensure the clarity of survey questions and resulted in some modifications to the survey questions. With the permission of the author (Strachota, 2003) (see Appendix B), several modifications were made. (See Appendix D)
The survey instrument included 10 demographic question items, eight items to measure learner-instructor interaction, 28 items to measure learner-social media interaction, and 14 items to measure general course satisfaction. The instrument used a Likert-type scale of 1-4 with (4) strongly agree, (3) agree, (2) disagree, and (1) strongly disagree; 21 of the learner-social media interaction items also used a zero (0) value for those that were (N/A) not applicable. The not applicable items were discarded.
Population
The population of this study consisted of 424 undergraduate and graduate students enrolled in the participant college’s courses that were delivered using Moodle during the Summer and Fall 2012 semesters. The electronic survey was completed by 171 participants which consisted of 64% female and 36% male; 75% white and 25% minorities; 25% who participated on athletic teams and 75 % who did not participate on athletic teams; 93% undergraduates and 7% graduate students; and 89% full-time students and 11% part-time students.
Data Collection
I sent an email to the participating college to determine the contact person for
institutional research. Upon receiving this information I sent a request for permission to gather the data for this study. Upon receiving permission, I sent an email to the institution’s Moodle Administrator to alert him to the upcoming survey and to encourage him to assist in the notification of students surveyed.
A cover letter (see Appendix C) was included in the survey that informed participants about the purpose of the survey and directions for completing the survey. The survey was converted into an electronic form using the online Survey Monkey software program. The survey’s URL link was delivered electronically to the participant Moodle administrator where it was posted onto each of the course Moodle sites. At the end of week-1 and at week-2 a reminder was sent out to increase the response rate. Participants voluntarily completed the survey and the identity of each student was protected.
Data Analysis
The instrument questions used for this study gathered the following participant data: questions 1-10 gathered demographic data; questions 11-18 gathered the learner-instructor interaction data; questions 19-46 gathered the learner-social media interaction data; and questions 47-60 gathered the general course satisfaction data. Questions 11-18 and 26-60 required participants to indicate their level of agreement with the statements presented. Strongly agree was assigned a score of 4, agree was assigned a score of 3, disagree was assigned a score
After the data collection period, descriptive statistics means, and standard deviations, were analyzed for each research construct. Independent Samples t tests, Pearson Correlations, and one-way ANOVAs, were completed to determine if there were any statistically significant relationships among the demographic variables and responses. An independent samples t test was used to evaluate the null hypothesis for research questions 1, 2, 3, 10, 11, 12, 13, 14, 15, 16, 17, and 18. A Pearson correlation was used to evaluate the null hypothesis for research questions 4, 5, 6, 7, 8, 9, 25, 26, 27, 28, and 29. A one-way ANOVA was used to evaluate the null
hypothesis for research questions 19, 20, 21, 22, 23, and 24. IBM-SPSS 19 was used to analyze the data gathered by The Online Satisfaction Survey.
Summary
Chapter 3 detailed the research method used for this study, including the purpose, research questions and null hypotheses, instrumentation, population, data collection processes, and data analysis. Quantitative research methods were used to gather data on student satisfaction in online classes from the participating college.
CHAPTER 4 RESULTS
The purpose of this study was to determine the relationships between or among the mean general course satisfaction, learner-instructor interaction, and the learner-social media interaction scores of participants. The survey instrument used to collect the data for this study contained statements related to the three constructs researched and contained 10 demographic items, eight items to measure learner-instructor interaction, 28 items to measure learner-social interaction, and 14 items to measure general course satisfaction. The survey was posted on the participating college’s electronic course site for students to complete during July and October of 2012.
The study’s convenience sample of 171 participants consisted of 64% female and 36% male; 75% white and 25% minorities; 25% who participated on athletic teams and 75 % who did not participate on athletic teams; 93% undergraduates and 7% graduate students; and 89% full- time students and 11% part-time students.
Research Questions
Twenty-nine research questions were used to direct the focus of this study. In order to determine data relationships the 29 null hypotheses were used to answer the 29 research questions. Results of the evaluations are shown for each question.
Research Question 1. Is there a significant difference in the Learner-Instructor Interaction Scores between male and female students at the participating college?
Ho1: There is no significant difference in the Learner-Instructor Interaction Scores between male and female students at the participating college.
An independent samples t-test was conducted to evaluate the mean difference in learner-instructor interaction scores based on gender. Levene’s Test for Equality of Variances showed that equal variances could not be assumed, F(1,168) = 4.82, p = .030. Therefore, the independent samples t-test that did not assume equal variances was used. The independent samples t test,
t(144) = .83, p = .410, was not statistically significant. Therefore, the null hypothesis was
retained. The effect size as measured by 2 was small (<.01). That is less than 1% of the variance in learner-instructor interaction was accounted for by gender. The mean learner-instructor
interaction for males (M = 3.20, SD = 0.44) was slightly lower than the mean for females (M = 3.26, SD = 0.51). The 95% confidence interval for the difference in means was -.21 to .09. The boxplot for learner-instructor interaction by gender is shown in Figure 1.
ο = an observation between 1.5 times to 3.0 times the interquartile range
Figure 1. Boxplot for Learner-Instructor Interaction by Gender
108 62 N = Gender Female Male 4.5 4.0 3.5 3.0 2.5 2.0 1.5 L ear n er -I n st ru ct or I n te rac tion
Research Question 2. Is there a significant difference in the Learner-Social Media Interaction Scores between male and female students at the participating college?
Ho2: There is no significant difference in the Learner-Social Media Interaction Scores between male and female students at the participating college.
An independent samples t-test was conducted to evaluate the mean difference in learner-social media interaction scores based on gender. The independent samples t-test, t(133) = 2.50, p = .014, was statistically significant. Therefore, the null hypothesis was rejected. The effect size as measured by 2 was small (.04). That is 4% of the variance in learner-social media interaction was accounted for by gender. The mean learner-social media interaction for males (M = 2.90, SD
= .64) was just about the same as the mean for females (M = 3.17, SD = .59). The 95%
confidence interval for the difference in means was -.48 to -.06. The boxplot for learner-social media interaction by gender is shown in Figure 2.
ο = an observation between 1.5 times to 3.0 times the interquartile range
Figure 2. Boxplot for Learner-Social Media Interaction by Gender
Research Question 3. Is there a significant difference in the General Course Satisfaction Scores between male and female students at the participating college?
Ho3 There is no significant difference in the General Course Satisfaction Scores between male and female students at the participating college.
An independent samples t-test was conducted to evaluate the difference between mean scores in General Course Satisfaction based on gender. The independent samples t-test, t(156) = 1.80, p = .074, was not statistically significant. Therefore, the null hypothesis was retained. The effect size, as measured by 2 was small (.02). That is 2% of the variance in general course satisfaction was accounted for by gender. The mean general course satisfaction for males (M =
83 52 N = Gender Female Male L ear n er -S oc ial M ed ia In te rac tion 4.5 4.0 3.5 3.0 2.5 2.0 1.5 1.0 .5
